Instructions to use microsoft/deberta-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-xlarge-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-xlarge-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.8377728
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Add TF weights (#1)
- Add TF weights (47de41bd0d1919fc7aeccf414e6369f296d3b4be) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>5b07a90
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xlarge mnli model
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DeBERTa XLarge MNLI model
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DeBERTa XLarge MNLI model
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DeBERTa XLarge MNLI model
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Create deberta xlarge 750M model fine-tuned with mnli
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